Trip destination prediction based on multi-day GPS data

Zong, Fang; Tian, Yongda; He, Yanan; Tang, Jinjun; Lv, Jianyu

doi:10.1016/j.physa.2018.09.090

Cited by 64 publications

(34 citation statements)

References 17 publications

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“…More details can be found in [36]. The Apriori algorithm assumes that the nonempty subsets of a frequent itemset are frequent itemsets, which greatly reduces the model computational consumption, [29,[37][38][39]. Several itemset types of PSC inspection data are null (i.e., the ship deficiency information is deliberately discarded due to data sensitivity).…”

Section: Dqcpea Algorithm For Identifying Ship Deficiencymentioning

confidence: 99%

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Chen

et al. 2020

PLoS ONE

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Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

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Section: Dqcpea Algorithm For Identifying Ship Deficiencymentioning

confidence: 99%

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Chen

et al. 2020

PLoS ONE

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“…Thus, future behavior is likely to be similar to previous behavior if the (latent) inertia is strong. Habit and inertia have also been extensively studied in the transport literature within a microeconomic approach, in the context of mode of mode (Bamberg et al, 2003;Cantillo et al, 2007;Chatterjee, 2011;Cherchi et al, 2014;Cherchi and Manca, 2011;Gardner, 2009;Golob et al, 1997;Gärling and Axhausen, 2003;Sharmeen and Timmermans, 2014;Srinivasan and Bhargavi, 2007;Yáñez et al, 2009), vehicle purchase (Bauer, 2018;Jansson et al, 2009), car engine type (Valeri and Cherchi, 2016), destination (Zong et al, 2019), route (Bogers et al, 2005;He et al, 2014;Prato et al, 2012), residential location (Ralph and Brown, 2017) and parking choice (van der Waerden et al, 2015). Cherchi et al (2014) have studied the role of habitual behavior in mode choice, using a hybrid approach that assumes that inertia is revealed by past behavior but recognizes that past behavior is only an indicator of habitual behavior, the true process behind the formation of habitual behavior being latent.…”

Section: Introductionmentioning

confidence: 99%

The habit-driven life: Accounting for inertia in departure time choices for commuting trips

Thorhauge

Swait́

Cherchi

2020

Transportation Research Part A: Policy and Practice

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This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals' inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected.

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“…For example, a spatial econometric model is widely used to explore the relationships between travel demand and a set of factors [16][17][18][19][20][21]. From the temporal perspective, applying time series models or dividing time into several periods is the most commonly used approach to understand the variations of travel demand at different times, such as weekdays and weekends, the morning peak, the evening peak and late at night [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, many spatio-temporal data can be expressed as time series data. Therefore, the GWR can also be used to clarify the spatio-temporal variations by combining the technique of time series mining [22]. As a result, certain patterns and regularities of travel demand in different zones at different time periods can be explored.…”

Section: Introductionmentioning

confidence: 99%

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

et al. 2019

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Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

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Trip destination prediction based on multi-day GPS data

Cited by 64 publications

References 17 publications

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Mining ship deficiency correlations from historical port state control (PSC) inspection data

The habit-driven life: Accounting for inertia in departure time choices for commuting trips

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

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